This reference text explores cutting edge research into the detection of breast cancer using Microwave Imaging. Early breast cancer detection is vital for reducing mortality rates. Within this book Microwave scattering and microwave imaging based cancer detection are analysed as well as breast anatomy and breast cancer types. The book discusses 3-D level set based optimization as well as the Finite difference time domain (FDTD) technique. Advanced methods in image reconstruction techniques and Group Theory are explained with application to computation reduction. Machine learning-based advanced methods are also described for breast cancer detection. This book is highly useful for the academic community working in biomedical imaging, electromagnetic and microwave imaging, breast cancer imaging, inverse scattering and optimization. Key Features: Breast cancer screening techniques are described and with advantages and disadvantages Multiple frequency inverse scattering is discussed Microwave imaging basics with detection analysis are explained in detail Includes 3-D level set based optimization Presents advanced methods on image-based reconstruction techniques

This book collates past and current research on one of the most promising emerging modalities for breast cancer detection. Readers will discover how, as a standalone technology or in conjunction with another modality, microwave imaging has the potential to provide reliable, safe and comfortable breast exams at low cost. Current breast imaging modalities include X- ray, Ultrasound, Magnetic Resonance Imaging, and Positron Emission Tomography. Each of these methods suffers from limitations, including poor sensitivity or specificity, high cost, patient discomfort, and exposure to potentially harmful ionising radiation. Microwave breast imaging is based on a contrast in the dielectric properties of breast tissue that exists at microwave frequencies. The book begins by considering the anatomy and dielectric properties of the breast, contrasting historical and recent studies. Next, radar-based breast imaging algorithms are discussed, encompassing both early-stage artefact removal, and data independent and adaptive beamforming algorithms. In a similar fashion, microwave tomographic reconstruction algorithms are reviewed in the following chapter, introducing the reader to both the fundamental and more advanced algorithms. Apart from imaging, the book also reviews research efforts in extracting clinically useful information from the Radar Target Signature of breast tumours, which is used to classify tumours as either benign or malignant. Finally, the book concludes by describing the current state of the art in terms of prototype microwave breast imaging systems, with a particular emphasis on those which have progressed to the clinical evaluation stage. This work is motivated by the fact that breast cancer is one of the leading causes of death amongst women in Europe and the US, and the second most common cancer in the world today. Such an important area of research will appeal to many scholars and practitioners.p>

Breast microwave sensing (BMS) is an emerging modality that has the potential to be used as a breast cancer screening technique but challenges remain before the modality is suitable for clinical use. Improvements to image-based and machine learning tumor-detection methods are required. This thesis presents novel improvements in image reconstruction and machine learning methods. This work presents the development of the largest open-access experimental dataset in the published BMS literature to-date, the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID). The impact of the inverse chirp z-transform (ICZT) on radar-based image reconstruction was compared to that of the standard inverse discrete Fourier transform using a subset of this dataset. The ICZT was found to reduce image artifacts, improve image contrast, and increase tumor-detection in reconstructions. A novel reconstruction method, the iterative delay-and-sum (itDAS) beamformer, was compared to two literature standard approaches. The novel method improved image contrast by as much as 249% on average and allows for the implementation of correction factors to improve the radar signal model used in the literature standard algorithms. Three correction factors were examined and modeling the output pulse of the BMS system significantly increased the contrast of itDAS reconstructions. The diagnostic capability of machine learning methods in BMS was investigated using UM-BMID. The area under the curve of the receiver operating characteristic curve of a convolutional neural network was estimated to be between (76 ± 3)% and (91 ± 3)%, where the upper estimate is obtained when the testing set is constrained to consist of phantoms with breast volumes that are within the breast volume bounds of the training set and when the tumor is located at the same vertical position as the antennas. This thesis has set the stage for future large-scale analyses in BMS through the development of the first and largest open-access dataset in the published literature and through the promising results obtained with the application of machine learning methods and the novel itDAS beamformer.

The conventional breast cancer detection methods have limitations ranging from ionizing radiations, low specificity to high cost. These limitations make way for a suitable alternative called Microwave Imaging, as a screening technique in the detection of breast cancer. The discernible differences between the benign, malignant and healthy breast tissues and the ability to overcome the harmful effects of ionizing radiations make microwave imaging, a feasible breast cancer detection technique. Earlier studies have shown the variation of electrical properties of healthy and malignant tissues as a function of frequency and hence stimulates high bandwidth requirement. A Ultrawideband, Wideband and Narrowband arrays have been designed, simulated and optimized for high (44%), medium (33%) and low (7%) bandwidths respectively, using the EM (electromagnetic software) called FEKO. These arrays are then used to illuminate the breast model (phantom) and the received backscattered signals are obtained in the near field for each case. The Microwave Imaging via Space-Time (MIST) beamforming algorithm in the frequency domain, is next applied to these near field backscattered monostatic frequency response signals for the image reconstruction of the breast model. The main purpose of this investigation is to access the impact of bandwidth and implement a novel imaging technique for use in the early detection of breast cancer. Earlier studies show the implementation of the MIST imaging algorithm on the time domain signals via a frequency domain beamformer. The performance evaluation of the imaging algorithm on the frequency response signals has been carried out in the frequency domain. The energy profile of the breast in the spatial domain is created via the frequency domain Parseval’s theorem. The beamformer weights calculated using these the MIST algorithm (not including the effect of the skin) has been calculated for Ultrawideband, Wideband and Narrowband arrays, respectively. Quality metrics such as dynamic range, radiometric resolution etc. are also evaluated for all the three types of arrays.

This book watches out for the issues on making moves for chest radiology in carcinoma of the chest. It focuses on all parts of radiological approaches to manage the breast illness, be it light (optical), sound (ultrasound), interest, microwave, electrical impedance, blend of these modalities, and a section of the incredibly intense issues on computer-aided detection. The dedication of the eminent analysts in this book has incorporated a lot of energy for the people who are adequately drawn in with the clinical organization of this ailment and also for the students of radiology and surgery alike. This book will definitely be appreciated and well taken by the surgeons, radiologists, and other professionals involved in this field. The contributions are excellent in terms of diagnostic approach by radiological means and would certainly be a step forward in making it possible to reach to a conclusive diagnosis of breast cancer much before it becomes inoperable. The chapters included will further our knowledge and to the best of my belief will make things easier and definable in terms of diagnosis of breast cancer.

Medical imaging has been transformed over the past 30 years by the advent of computerized tomography (CT), magnetic resonance imaging (MRI), and various advances in x-ray and ultrasonic techniques. An enabling force behind this progress has been the (so far) exponentially increasing power of computers, which has made it practical to explore fundamentally new approaches. In particular, what our group terms "model-based" modalities-which produce tissue property images from data using nonlinear, iterative numerical modeling techniques-have become increasingly feasible. Alternative Breast Imaging: Four Model-Based Approaches explores our research on four such modalities, particularly with regard to imaging of the breast: (1) MR elastography (MRE), (2) electrical impedance spectroscopy (EIS), (3) microwave imaging spectroscopy (MIS), and (4) near infrared spectroscopic imaging (NIS). Chapter 1 introduces the present state of breast imaging and discusses how our alternative modalities can contribute to the field. Chapter 2 looks at the computational common ground shared by all four modalities. Chapters 2 through 10 are devoted to the four modalities, with each modality being discussed first in a theory chapter and then in an implementation-and-results chapter. The eleventh and final chapter discusses statistical methods for image analysis in the context of these four alternative imaging modalities. Imaging for the detection of breast cancer is a particularly interesting and relevant application of the four imaging modalities discussed in this book. Breast cancer is an extremely common health problem for women; the National Cancer Institute estimates that one in eight US women will develop breast cancer at least once in her lifetime. Yet the efficacy of the standard (and notoriously uncomfortable) early-detection test, the x-ray mammogram, has been disputed of late, especially for younger women. Conditions are thus ripe for the development of affordable techniques that replace or complement mammography. The breast is both anatomically accessible and small enough that the computing power required to model it, is affordable.

We overview the research trend on microwave imaging for early breast cancer detection. The technologies have two categories: ultra-wide band (UWB) radar that reconstructs the scattering power distribution in the breast and inverse scattering problem that reconstructs the dielectric properties distribution. We have developed a clinical equipment using UWB radar and carried out clinical test 4 years ago. Through the experiments, we concluded that the UWB radar was insufficient for the clinical equipment, because the UWB radar cannot discriminate cancerous tumor and other lesions. Therefore, we have been studying inverse scattering. It is a challenging task to develop an equipment using inverse scattering technologies. We have proposed a microwave mammography that has four features: (1) sensor with breast fixing by absorption, (2) small sensor with multipolarization, (3) image reconstruction program linking the commercial EM simulator, and (4) hybrid imaging method using UWB radar and inverse scattering.